Hands-On Transfer Learning with Python

Loading up data and models

The next steps include loading up the necessary data, model, and other assets from disk into memory. We first load up our test dataset and our trained deep learning models:

# load test dataset 
test_df = pd.read_csv('image_test_dataset.tsv', delimiter='\t') 
 
# load the models 
from keras.models import load_model 
 
model1 = load_model('ic_model_rmsprop_b256ep30.h5') 
model2 = load_model('ic_model_rmsprop_b256ep50.h5') 

We now need to load up necessary metadata assets, such as the image features we extracted before for our test data, and our vocabulary metadata:

from sklearn.externals import joblib tl_img_feature_map = joblib.load('transfer_learn_img_features.pkl') vocab_metadata = joblib.load('vocabulary_metadata.pkl') ...